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K-Histograms: An Efficient Clustering Algorithm for Categorical Dataset

Zengyou He·Xiaofei Xu·Shengchun Deng·Bin Dong
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Abstract

Clustering categorical data is an integral part of data mining and has attracted much attention recently. In this paper, we present k-histogram, a new efficient algorithm for clustering categorical data. The k-histogram algorithm extends the k-means algorithm to categorical domain by replacing the means of clusters with histograms, and dynamically updates histograms in the clustering process. Experimental results on real datasets show that k-histogram algorithm can produce better clustering results than k-modes algorithm, the one related with our work most closely.

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Zengyou He, Xiaofei Xu, Shengchun Deng, Bin Dong. 2005-09-13. K-Histograms: An Efficient Clustering Algorithm for Categorical Dataset. https://arxiv.org/abs/cs/0509033

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